Upload all models and assets for ka (latest)

.gitattributes

@@ -33,3 +33,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+visualizations/embedding_similarity.png filter=lfs diff=lfs merge=lfs -text
+visualizations/embedding_tsne_multilingual.png filter=lfs diff=lfs merge=lfs -text
+visualizations/performance_dashboard.png filter=lfs diff=lfs merge=lfs -text
+visualizations/position_encoding_comparison.png filter=lfs diff=lfs merge=lfs -text
+visualizations/tsne_sentences.png filter=lfs diff=lfs merge=lfs -text
+visualizations/tsne_words.png filter=lfs diff=lfs merge=lfs -text
+visualizations/zipf_law.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,773 @@
+---
+language: ka
+language_name: Georgian
+language_family: kartvelian
+tags:
+  - wikilangs
+  - nlp
+  - tokenizer
+  - embeddings
+  - n-gram
+  - markov
+  - wikipedia
+  - feature-extraction
+  - sentence-similarity
+  - tokenization
+  - n-grams
+  - markov-chain
+  - text-mining
+  - fasttext
+  - babelvec
+  - vocabulous
+  - vocabulary
+  - monolingual
+  - family-kartvelian
+license: mit
+library_name: wikilangs
+pipeline_tag: text-generation
+datasets:
+  - omarkamali/wikipedia-monthly
+dataset_info:
+  name: wikipedia-monthly
+  description: Monthly snapshots of Wikipedia articles across 300+ languages
+metrics:
+  - name: best_compression_ratio
+    type: compression
+    value: 5.034
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7869
+  - name: vocabulary_size
+    type: vocab
+    value: 0
+generated: 2026-01-10
+---
+
+# Georgian - Wikilangs Models
+## Comprehensive Research Report & Full Ablation Study
+
+This repository contains NLP models trained and evaluated by Wikilangs, specifically on **Georgian** Wikipedia data.
+We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings.
+
+## 📋 Repository Contents
+
+### Models & Assets
+
+- Tokenizers (8k, 16k, 32k, 64k)
+- N-gram models (2, 3, 4, 5-gram)
+- Markov chains (context of 1, 2, 3, 4 and 5)
+- Subword N-gram and Markov chains
+- Embeddings in various sizes and dimensions (aligned and unaligned)
+- Language Vocabulary
+- Language Statistics
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Analysis and Evaluation
+
+- [1. Tokenizer Evaluation](#1-tokenizer-evaluation)
+- [2. N-gram Model Evaluation](#2-n-gram-model-evaluation)
+- [3. Markov Chain Evaluation](#3-markov-chain-evaluation)
+- [4. Vocabulary Analysis](#4-vocabulary-analysis)
+- [5. Word Embeddings Evaluation](#5-word-embeddings-evaluation)
+- [6. Morphological Analysis (Experimental)](#6--morphological-analysis-experimental)
+- [7. Summary & Recommendations](#7-summary--recommendations)
+- [Metrics Glossary](#appendix-metrics-glossary--interpretation-guide)
+- [Visualizations Index](#visualizations-index)
+
+---
+## 1. Tokenizer Evaluation
+
+![Tokenizer Compression](visualizations/tokenizer_compression.png)
+
+![Tokenizer Fertility](visualizations/tokenizer_fertility.png)
+
+![Tokenizer OOV](visualizations/tokenizer_oov.png)
+
+![Total Tokens](visualizations/tokenizer_total_tokens.png)
+
+### Results
+
+| Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens |
+|------------|-------------|---------------|----------|--------------|
+| **8k** | 3.607x | 3.61 | 0.0743% | 1,129,507 |
+| **16k** | 4.126x | 4.13 | 0.0850% | 987,428 |
+| **32k** | 4.611x | 4.61 | 0.0950% | 883,432 |
+| **64k** | 5.034x 🏆 | 5.04 | 0.1037% | 809,192 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `ბა () — მეორე ასო არაბულ დამწერლობაში. ბა არის არაბული ვარიანტი ებრაული ბეთისა. ...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ბა ▁() ▁— ▁მეორე ▁ასო ▁არაბ ულ ▁დამწერ ლობაში . ... (+19 more)` | 29 |
+| 16k | `▁ბა ▁() ▁— ▁მეორე ▁ასო ▁არაბულ ▁დამწერ ლობაში . ▁ბა ... (+16 more)` | 26 |
+| 32k | `▁ბა ▁() ▁— ▁მეორე ▁ასო ▁არაბულ ▁დამწერ ლობაში . ▁ბა ... (+13 more)` | 23 |
+| 64k | `▁ბა ▁() ▁— ▁მეორე ▁ასო ▁არაბულ ▁დამწერლობაში . ▁ბა ▁არის ... (+12 more)` | 22 |
+
+**Sample 2:** `ხათინბულაყი () — სოფელი აზერბაიჯანში, ფუზულის რაიონში. რესურსები ინტერნეტში რაიო...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁ხ ათინ ბ ულა ყ ი ▁() ▁— ▁სოფელი ▁აზერბაიჯანში ... (+10 more)` | 20 |
+| 16k | `▁ხ ათინ ბ ულა ყი ▁() ▁— ▁სოფელი ▁აზერბაიჯანში , ... (+9 more)` | 19 |
+| 32k | `▁ხ ათინ ბულა ყი ▁() ▁— ▁სოფელი ▁აზერბაიჯანში , ▁ფუზულის ... (+6 more)` | 16 |
+| 64k | `▁ხ ათინ ბულაყი ▁() ▁— ▁სოფელი ▁აზერბაიჯანში , ▁ფუზულის ▁რაიონში ... (+5 more)` | 15 |
+
+**Sample 3:** `მერდინლი () — სოფელი აზერბაიჯანში, ფუზულის რაიონში. რესურსები ინტერნეტში რაიონის...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁მერ დინ ლი ▁() ▁— ▁სოფელი ▁აზერბაიჯანში , ▁ფ უზ ... (+7 more)` | 17 |
+| 16k | `▁მერ დინ ლი ▁() ▁— ▁სოფელი ▁აზერბაიჯანში , ▁ფ უზ ... (+7 more)` | 17 |
+| 32k | `▁მერ დინ ლი ▁() ▁— ▁სოფელი ▁აზერბაიჯანში , ▁ფუზულის ▁რაიონში ... (+5 more)` | 15 |
+| 64k | `▁მერ დინ ლი ▁() ▁— ▁სოფელი ▁აზერბაიჯანში , ▁ფუზულის ▁რაიონში ... (+5 more)` | 15 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 5.034x compression
+- **Lowest UNK Rate:** 8k with 0.0743% unknown tokens
+- **Trade-off:** Larger vocabularies improve compression but increase model size
+- **Recommendation:** 32k vocabulary provides optimal balance for production use
+
+---
+## 2. N-gram Model Evaluation
+
+![N-gram Perplexity](visualizations/ngram_perplexity.png)
+
+![N-gram Unique](visualizations/ngram_unique.png)
+
+![N-gram Coverage](visualizations/ngram_coverage.png)
+
+### Results
+
+| N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
+|--------|---------|------------|---------|----------------|------------------|-------------------|
+| **2-gram** | Word | 211,247 | 17.69 | 839,931 | 5.0% | 14.8% |
+| **2-gram** | Subword | 423 🏆 | 8.73 | 18,881 | 58.4% | 96.6% |
+| **3-gram** | Word | 297,944 | 18.18 | 972,504 | 4.8% | 13.7% |
+| **3-gram** | Subword | 3,918 | 11.94 | 159,884 | 21.8% | 60.3% |
+| **4-gram** | Word | 509,839 | 18.96 | 1,548,052 | 4.2% | 12.7% |
+| **4-gram** | Subword | 22,811 | 14.48 | 936,455 | 10.0% | 32.5% |
+| **5-gram** | Word | 359,649 | 18.46 | 1,104,097 | 4.9% | 14.4% |
+| **5-gram** | Subword | 88,814 | 16.44 | 2,995,844 | 5.2% | 19.7% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `რესურსები ინტერნეტში` | 100,310 |
+| 2 | `იხილეთ აგრეთვე` | 38,767 |
+| 3 | `ერთ ერთი` | 38,192 |
+| 4 | `და სხვა` | 20,983 |
+| 5 | `of the` | 20,829 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `წლის მონაცემებით მოსახლეობა` | 9,844 |
+| 2 | `რესურსები ინტერნეტში სქოლიო` | 8,451 |
+| 3 | `მიუხედავად იმისა რომ` | 8,239 |
+| 4 | `ოფიციალური საიტი სქოლიო` | 7,888 |
+| 5 | `მდებარეობს ზღვის დონიდან` | 7,694 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `კაცი წ იხილეთ აგრეთვე` | 6,676 |
+| 2 | `მუნიციპალიტეტის ოფიციალური საიტი სქოლიო` | 5,942 |
+| 3 | `ოფიციალური საიტი სქოლიო მუნიციპალიტეტები` | 5,878 |
+| 4 | `კმ ია იხილეთ აგრეთვე` | 5,763 |
+| 5 | `ჭიანჭველასებრთა ერთ ერთი ნაირსახეობა` | 5,614 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `მუნიციპალიტეტის ოფიციალური საიტი სქოლიო მუნიციპალიტეტები` | 5,860 |
+| 2 | `კლასის ჭიანჭველასებრთა ერთ ერთი ნაირსახეობა` | 5,614 |
+| 3 | `მწერთა კლასის ჭიანჭველასებრთა ერთ ერთი` | 5,614 |
+| 4 | `ფეხსახსრიანთა ტიპის მწერთა კლასის ჭიანჭველასებრთა` | 5,614 |
+| 5 | `ტიპის მწერთა კლასის ჭიანჭველასებრთა ერთ` | 5,614 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ს _` | 7,040,665 |
+| 2 | `ი ს` | 7,006,427 |
+| 3 | `ი _` | 6,580,536 |
+| 4 | `ა _` | 4,984,758 |
+| 5 | `ე ბ` | 4,979,269 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ი ს _` | 5,017,584 |
+| 2 | `ე ბ ი` | 2,375,826 |
+| 3 | `_ დ ა` | 2,051,321 |
+| 4 | `_ ს ა` | 1,739,078 |
+| 5 | `დ ა _` | 1,650,928 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ დ ა _` | 1,089,218 |
+| 2 | `ბ ი ს _` | 984,947 |
+| 3 | `ე ბ ი ს` | 883,610 |
+| 4 | `ე ბ ი _` | 738,436 |
+| 5 | `ი ს _ მ` | 733,039 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `ე ბ ი ს _` | 734,333 |
+| 2 | `ა _ დ ა _` | 430,206 |
+| 3 | `, _ რ ო მ` | 406,399 |
+| 4 | `ი ს _ ს ა` | 350,334 |
+| 5 | `ე ბ უ ლ ი` | 307,583 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 423
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~20% of corpus
+- **Recommendation:** 4-gram or 5-gram for best predictive performance
+
+---
+## 3. Markov Chain Evaluation
+
+![Markov Entropy](visualizations/markov_entropy.png)
+
+![Markov Contexts](visualizations/markov_contexts.png)
+
+![Markov Branching](visualizations/markov_branching.png)
+
+### Results
+
+| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
+|---------|---------|-------------|------------|------------------|-----------------|----------------|
+| **1** | Word | 0.9133 | 1.883 | 9.96 | 1,644,802 | 8.7% |
+| **1** | Subword | 1.0685 | 2.097 | 7.59 | 8,326 | 0.0% |
+| **2** | Word | 0.2778 | 1.212 | 1.75 | 16,356,120 | 72.2% |
+| **2** | Subword | 0.7948 | 1.735 | 5.53 | 63,087 | 20.5% |
+| **3** | Word | 0.0801 | 1.057 | 1.15 | 28,502,543 | 92.0% |
+| **3** | Subword | 0.7992 | 1.740 | 4.61 | 348,410 | 20.1% |
+| **4** | Word | 0.0282 🏆 | 1.020 | 1.04 | 32,622,645 | 97.2% |
+| **4** | Subword | 0.7081 | 1.634 | 3.48 | 1,604,353 | 29.2% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `და ფესტივალის roadburn festival at war chapter 8 6 7 489 ხელოვნებაში გაწაფული და დასავლური მარიული`
+2. `წლის აპრილში ჟურნალ modern philology v საუკუნეებით ძეგლი ვატერტონ გლასიერის მშვიდობის განმტკიცებაში ...`
+3. `წელს ტაძრის კრამიტი ცემენტქვიშის ხსნარის სიმკვრივე 0 1 ნოემბერი დეკემბერი მუნიციპალიტეტში ჭალის მუხი...`
+
+**Context Size 2:**
+
+1. `რესურსები ინტერნეტში ოფიციალური საიტი სქოლიო მუნიციპალიტეტები ქალაქები და წელს ის მიიწვიეს პეტერბურგ...`
+2. `იხილეთ აგრეთვე მინას ჟერაისი ბრაზილიის შტატები რესურსები ინტერნეტში ოფიციალური საიტი ჰარპერი მუსიკოს...`
+3. `ერთ ერთი ინიციატორი სული და ამ დროიდან ციხესიმაგრემ შეწყვიტა მხოლოდ მაშინ ექვემდებარება თუ ოჯახს საკ...`
+
+**Context Size 3:**
+
+1. `წლის მონაცემებით მოსახლეობა 84 469 ადამიანს შეადგენდა ფართობი 358 კმ მოსახლეობა 61 418 ადამიანი წლის...`
+2. `მიუხედავად იმისა რომ მპა მედიკამენტურად ვერ იკურნება მედიკამენტები შეიძლება გამოვიყენოთ სიმპტომების ...`
+3. `რესურსები ინტერნეტში სქოლიო მომღერლები 25 აგვისტო records ის შემსრულებლები records ის შემსრულებლები ...`
+
+**Context Size 4:**
+
+1. `კაცი წ იხილეთ აგრეთვე სევილიის მუნიციპალიტეტები რესურსები ინტერნეტში ალანისის მუნიციპალიტეტის ოფიცია...`
+2. `მუნიციპალიტეტის ოფიციალური საიტი სქოლიო მუნიციპალიტეტები ქალაქები გვერდები გვარის შემცველი სიებით გვ...`
+3. `კმ ია იხილეთ აგრეთვე კოლუმბიის ქალაქების სია რესურსები ინტერნეტში ქალაქის მთავრობის საიტი ქალაქის სა...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_მეენა;_წლოგას_კ`
+2. `ა_მოს_მდისაღობულ`
+3. `ი���ა_ც_ბოჰას_სტჩი`
+
+**Context Size 2:**
+
+1. `ს_ნიერალიერგიერის`
+2. `ის_ოქმნინის_მკვე_`
+3. `ი_ერთემელი,_nalos`
+
+**Context Size 3:**
+
+1. `ის_ჯვრია_დაში,_რომ`
+2. `ების_წარმოდის_კრებ`
+3. `_დაიქცა._კიდერილთა`
+
+**Context Size 4:**
+
+1. `_და_ამ_პროვი._თამაშ`
+2. `ბის_საჭირო._წელს._ხ`
+3. `ებისამე_სირიის_წყალ`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 97.2% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (1,604,353 contexts)
+- **Recommendation:** Context-3 or Context-4 for text generation
+
+---
+## 4. Vocabulary Analysis
+
+![Zipf's Law](visualizations/zipf_law.png)
+
+![Top Words](visualizations/top20_words.png)
+
+![Coverage Curve](visualizations/vocab_coverage.png)
+
+### Statistics
+
+| Metric | Value |
+|--------|-------|
+| Vocabulary Size | 715,127 |
+| Total Tokens | 37,347,310 |
+| Mean Frequency | 52.22 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 1624.79 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | და | 1,097,500 |
+| 2 | წლის | 288,743 |
+| 3 | წელს | 254,559 |
+| 4 | იყო | 188,838 |
+| 5 | ის | 162,453 |
+| 6 | რომელიც | 141,611 |
+| 7 | the | 128,418 |
+| 8 | რომ | 124,976 |
+| 9 | 1 | 122,280 |
+| 10 | მისი | 118,080 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | pbsuccess | 2 |
+| 2 | მანგუსი | 2 |
+| 3 | ბარნჰამი | 2 |
+| 4 | რაჩაკის | 2 |
+| 5 | peig | 2 |
+| 6 | ლემსის | 2 |
+| 7 | smap | 2 |
+| 8 | კამიტის | 2 |
+| 9 | ვილაკამპას | 2 |
+| 10 | დევორეს | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 0.9374 |
+| R² (Goodness of Fit) | 0.993145 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 19.3% |
+| Top 1,000 | 42.2% |
+| Top 5,000 | 62.0% |
+| Top 10,000 | 70.3% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9931 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 19.3% of corpus
+- **Long Tail:** 705,127 words needed for remaining 29.7% coverage
+
+---
+## 5. Word Embeddings Evaluation
+
+![Embedding Isotropy](visualizations/embedding_isotropy.png)
+
+![Similarity Matrix](visualizations/embedding_similarity.png)
+
+![t-SNE Words](visualizations/tsne_words.png)
+
+![t-SNE Sentences](visualizations/tsne_sentences.png)
+
+
+### 5.1 Cross-Lingual Alignment
+
+![Alignment Quality](visualizations/embedding_alignment_quality.png)
+
+![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.7869 | 0.3479 | N/A | N/A |
+| **mono_64d** | 64 | 0.7389 | 0.2989 | N/A | N/A |
+| **mono_128d** | 128 | 0.6243 | 0.2604 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7869 🏆 | 0.3607 | 0.1100 | 0.4300 |
+| **aligned_64d** | 64 | 0.7389 | 0.3031 | 0.2300 | 0.6300 |
+| **aligned_128d** | 128 | 0.6243 | 0.2636 | 0.2980 | 0.7120 |
+
+### Key Findings
+
+- **Best Isotropy:** aligned_32d with 0.7869 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3057. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 29.8% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data.
+
+### 6.1 Productivity & Complexity
+
+| Metric | Value | Interpretation | Recommendation |
+|--------|-------|----------------|----------------|
+| Productivity Index | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **-0.271** | Low formulaic content | - |
+
+### 6.2 Affix Inventory (Productive Units)
+
+These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts.
+
+#### Productive Prefixes
+| Prefix | Examples |
+|--------|----------|
+| `-სა` | სატომთაშ��რისო, საგანძურისა, სამართლიანი |
+| `-ა` | ავტომატიზირებულ, აგროტექნიკის, აცაურას |
+| `-გა` | გამოვლინებებამდე, გამაცალკევებელი, გათხრებისთვის |
+| `-და` | დაგვანებეთ, დაკრძალულ, დაპყრობლი |
+| `-მ` | მოკავშირეობაზე, მავრიკიოსს, მანიშნებელ |
+| `-მა` | მავრიკიოსს, მანიშნებელ, მარჯანიშვილი |
+| `-მო` | მოკავშირეობაზე, მომქნევი, მომლოცველთაგან |
+| `-ს` | სატომთაშორისო, საგანძურისა, სამართლიანი |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-ს` | დრეიკს, მავრიკიოსს, დრაჩას |
+| `-ი` | ბანიშევსკი, ეგზემპლარში, სამართლიანი |
+| `-ის` | პარმელის, იასარის, ყუსარის |
+| `-ა` | კირქვისაა, ზედამიოცენისა, საგანძურისა |
+| `-ან` | დიაკონთან, ერდმან, ბრიტანელებისაგან |
+| `-ნ` | დიაკონთან, ერდმან, ბრიტანელებისაგან |
+| `-ლი` | მარჯანიშვილი, ჩამოშორებული, დაპყრობლი |
+| `-სა` | ზედამიოცენისა, საგანძურისა, ტყვიავისა |
+
+### 6.3 Bound Stems (Lexical Roots)
+
+Bound stems are high-frequency subword units that are semantically cohesive but rarely appear as standalone words. These often correspond to the 'core' of a word that requires inflection or derivation to be valid.
+
+| Stem | Cohesion | Substitutability | Examples |
+|------|----------|------------------|----------|
+| `ეთის` | 1.85x | 194 contexts | ჩეთის, შეთის, ბეთის |
+| `ელებ` | 1.48x | 550 contexts | ელები, კელებ, ჯელებ |
+| `მდეგ` | 2.33x | 52 contexts | ემდეგ, შამდეგ, დამდეგ |
+| `ენებ` | 1.41x | 544 contexts | ენება, ენები, ენებს |
+| `ებულ` | 1.59x | 250 contexts | ქებულ, კრებულ, ქებული |
+| `დგენ` | 1.75x | 134 contexts | ადგენს, ვადგენ, უდგენს |
+| `ართვ` | 1.68x | 147 contexts | ქართვ, მართვე, ჩართვა |
+| `რთვე` | 1.81x | 96 contexts | მართვე, რთველს, ქართვე |
+| `ომელ` | 1.63x | 118 contexts | ტომელ, რომელ, დომელი |
+| `ნტერ` | 1.51x | 148 contexts | უნტერ, ინტერ, ენტერ |
+| `ავლე` | 1.43x | 180 contexts | სავლე, ავლენ, პავლე |
+| `ალაქ` | 1.58x | 106 contexts | ქალაქ, მალაქ, დალაქს |
+
+### 6.4 Affix Compatibility (Co-occurrence)
+
+This table shows which prefixes and suffixes most frequently co-occur on the same stems, revealing the 'stacking' rules of the language's morphology.
+
+| Prefix | Suffix | Frequency | Examples |
+|--------|--------|-----------|----------|
+| `-მ` | `-ს` | 186 words | მონოტონურობის, მოსულიშვილს |
+| `-მ` | `-ი` | 167 words | მხატვრებში, მელანქოლიკური |
+| `-მ` | `-ა` | 129 words | მღებავთა, მანდილოსნებმა |
+| `-ა` | `-ი` | 124 words | ალტენბურგში, ატკინი |
+| `-ა` | `-ს` | 120 words | ალდეს, ანდერსონის |
+| `-გა` | `-ა` | 111 words | გამეგზავრა, გაკრიტიკება |
+| `-მ` | `-ის` | 104 words | მონოტონურობის, მუსანდამის |
+| `-ა` | `-ა` | 93 words | აბრევიაცია, აიუთაია |
+| `-ს` | `-ს` | 88 words | სისლეის, სკიარას |
+| `-ს` | `-ი` | 82 words | საფრთხეში, სტერეოსკოპული |
+
+### 6.5 Recursive Morpheme Segmentation
+
+Using **Recursive Hierarchical Substitutability**, we decompose complex words into their constituent morphemes. This approach handles nested affixes (e.g., `prefix-prefix-root-suffix`).
+
+| Word | Suggested Split | Confidence | Stem |
+|------|-----------------|------------|------|
+| დაკარგვასა | **`დაკარგვა-ს-ა`** | 7.5 | `ს` |
+| პედიატრიასა | **`პედიატრია-ს-ა`** | 7.5 | `ს` |
+| ინდიკატორებია | **`ინდიკატორებ-ი-ა`** | 7.5 | `ი` |
+| კარნახობდათ | **`კარნახობდ-ა-თ`** | 7.5 | `ა` |
+| ნაციონალიზმსა | **`ნაციონალიზმ-ს-ა`** | 7.5 | `ს` |
+| მიმოხილვაზე | **`მიმოხილვ-ა-ზე`** | 7.5 | `ა` |
+| მეტყევეობაში | **`მეტყევეობ-ა-ში`** | 7.5 | `ა` |
+| არისტოკრატია | **`არისტოკრატ-ი-ა`** | 7.5 | `ი` |
+| გადაუხდელობას | **`გადაუხდელობ-ა-ს`** | 7.5 | `ა` |
+| провинцииოსო | **`провинцииო-ს-ო`** | 7.5 | `ს` |
+| ძეგლებსაც | **`ძეგლებ-ს-აც`** | 7.5 | `ს` |
+| კონფისკაციასა | **`კონფისკაცია-ს-ა`** | 7.5 | `ს` |
+| არაკატაკაში | **`არაკატაკ-ა-ში`** | 7.5 | `ა` |
+| მონოკრისტალი | **`მონოკრისტ-ა-ლი`** | 7.5 | `ა` |
+| ჰოლანდიას | **`ჰოლანდი-ა-ს`** | 7.5 | `ა` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language Georgian shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (5.03x) |
+| N-gram | **2-gram** | Lowest perplexity (423) |
+| Markov | **Context-4** | Highest predictability (97.2%) |
+| Embeddings | **100d** | Balanced semantic capture and isotropy |
+
+
+---
+## Appendix: Metrics Glossary & Interpretation Guide
+
+This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report.
+
+### Tokenizer Metrics
+
+**Compression Ratio**
+> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text.
+>
+> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average.
+>
+> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information.
+
+**Average Token Length (Fertility)**
+> *Definition:* Mean number of characters per token produced by the tokenizer.
+>
+> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length.
+>
+> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens.
+
+**Unknown Token Rate (OOV Rate)**
+> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent.
+>
+> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences.
+>
+> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback.
+
+### N-gram Model Metrics
+
+**Perplexity**
+> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction.
+>
+> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options.
+>
+> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size.
+
+**Entropy**
+> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy.
+>
+> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character.
+>
+> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases.
+
+**Coverage (Top-K)**
+> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams.
+>
+> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage.
+>
+> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text.
+
+### Markov Chain Metrics
+
+**Average Entropy**
+> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction.
+>
+> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations).
+>
+> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions.
+
+**Branching Factor**
+> *Definition:* Average number of unique next tokens observed for each context.
+>
+> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive).
+>
+> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains.
+
+**Predictability**
+> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are.
+>
+> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes.
+>
+> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output.
+
+### Vocabulary & Zipf's Law Metrics
+
+**Zipf's Coefficient**
+> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1.
+>
+> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare.
+>
+> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text.
+
+**R² (Coefficient of Determination)**
+> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1.
+>
+> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns.
+>
+> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora.
+
+**Vocabulary Coverage**
+> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words.
+>
+> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words.
+>
+> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary.
+
+### Word Embedding Metrics
+
+**Isotropy**
+> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values.
+>
+> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness.
+>
+> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy.
+
+**Average Norm**
+> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space.
+>
+> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained.
+>
+> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation).
+
+**Cosine Similarity**
+> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction).
+>
+> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings.
+>
+> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7.
+
+**t-SNE Visualization**
+> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization.
+>
+> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence.
+>
+> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure.
+
+### General Interpretation Guidelines
+
+1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer).
+2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate).
+3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification.
+4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature.
+5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages.
+
+
+### Visualizations Index
+
+| Visualization | Description |
+|---------------|-------------|
+| Tokenizer Compression | Compression ratios by vocabulary size |
+| Tokenizer Fertility | Average token length by vocabulary |
+| Tokenizer OOV | Unknown token rates |
+| Tokenizer Total Tokens | Total tokens by vocabulary |
+| N-gram Perplexity | Perplexity by n-gram size |
+| N-gram Entropy | Entropy by n-gram size |
+| N-gram Coverage | Top pattern coverage |
+| N-gram Unique | Unique n-gram counts |
+| Markov Entropy | Entropy by context size |
+| Markov Branching | Branching factor by context |
+| Markov Contexts | Unique context counts |
+| Zipf's Law | Frequency-rank distribution with fit |
+| Vocab Frequency | Word frequency distribution |
+| Top 20 Words | Most frequent words |
+| Vocab Coverage | Cumulative coverage curve |
+| Embedding Isotropy | Vector space uniformity |
+| Embedding Norms | Vector magnitude distribution |
+| Embedding Similarity | Word similarity heatmap |
+| Nearest Neighbors | Similar words for key terms |
+| t-SNE Words | 2D word embedding visualization |
+| t-SNE Sentences | 2D sentence embedding visualization |
+| Position Encoding | Encoding method comparison |
+| Model Sizes | Storage requirements |
+| Performance Dashboard | Comprehensive performance overview |
+
+---
+## About This Project
+
+### Data Source
+
+Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages.
+
+### Project
+
+A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language.
+
+### Maintainer
+
+[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+If you use these models in your research, please cite:
+
+```bibtex
+@misc{wikilangs2025,
+  author = {Kamali, Omar},
+  title = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year = {2025},
+  doi = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url = {https://huggingface.co/wikilangs}
+  institution = {Omneity Labs}
+}
+```
+
+### License
+
+MIT License - Free for academic and commercial use.
+
+### Links
+
+- 🌐 Website: [wikilangs.org](https://wikilangs.org)
+- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs)
+- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+---
+*Generated by Wikilangs Models Pipeline*
+
+*Report Date: 2026-01-10 11:10:47*

models/embeddings/aligned/ka_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:898f9fb76ca000be5045454d72c7866244fbed09345713fcaa37c10b2544cf44
+size 1468954504

models/embeddings/aligned/ka_128d.meta.json

@@ -0,0 +1 @@
+{"lang": "ka", "dim": 128, "max_seq_len": 512, "is_aligned": true}

models/embeddings/aligned/ka_128d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:81d422bc17633f2f590a251f8e7029406c9baf56ef5121923b1c07cd7efb377c
+size 65664

models/embeddings/aligned/ka_128d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "ka",
+  "dimension": 128,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 58406,
+  "vocab_size": 421071
+}

models/embeddings/aligned/ka_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff1ca535bd7ccdade008722c86bcb574d57f21e2ed000325570cc18118de3756
+size 377571976

models/embeddings/aligned/ka_32d.meta.json

@@ -0,0 +1 @@
+{"lang": "ka", "dim": 32, "max_seq_len": 512, "is_aligned": true}

models/embeddings/aligned/ka_32d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f3647bafcb9f3d1fc9dc3bc8ea0d9b36785f30927725dd897cccfbfaa88832cb
+size 4224

models/embeddings/aligned/ka_32d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "ka",
+  "dimension": 32,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 58406,
+  "vocab_size": 421071
+}

models/embeddings/aligned/ka_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:61d9ae428b660b7fccdf38b0f2289096a7d090120f81271237067b4a13e45d66
+size 741366152

models/embeddings/aligned/ka_64d.meta.json

@@ -0,0 +1 @@
+{"lang": "ka", "dim": 64, "max_seq_len": 512, "is_aligned": true}

models/embeddings/aligned/ka_64d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3172bb6c89c07dd317023ad3cc858a8a96fba544ba38cc1bfff500b22ad8f9c
+size 16512

models/embeddings/aligned/ka_64d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "ka",
+  "dimension": 64,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 58406,
+  "vocab_size": 421071
+}

models/embeddings/monolingual/ka_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:898f9fb76ca000be5045454d72c7866244fbed09345713fcaa37c10b2544cf44
+size 1468954504

models/embeddings/monolingual/ka_128d.meta.json

@@ -0,0 +1 @@
+{"lang": "ka", "dim": 128, "max_seq_len": 512, "is_aligned": false}

models/embeddings/monolingual/ka_128d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "ka",
+  "dimension": 128,
+  "version": "monolingual",
+  "training_params": {
+    "algorithm": "skipgram",
+    "min_count": 5,
+    "window": 5,
+    "negative": 5,
+    "epochs": 5,
+    "encoding_method": "rope",
+    "dim": 128,
+    "threads": 5
+  },
+  "vocab_size": 421071
+}

models/embeddings/monolingual/ka_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff1ca535bd7ccdade008722c86bcb574d57f21e2ed000325570cc18118de3756
+size 377571976

models/embeddings/monolingual/ka_32d.meta.json

@@ -0,0 +1 @@
+{"lang": "ka", "dim": 32, "max_seq_len": 512, "is_aligned": false}

models/embeddings/monolingual/ka_32d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "ka",
+  "dimension": 32,
+  "version": "monolingual",
+  "training_params": {
+    "algorithm": "skipgram",
+    "min_count": 5,
+    "window": 5,
+    "negative": 5,
+    "epochs": 5,
+    "encoding_method": "rope",
+    "dim": 32,
+    "threads": 5
+  },
+  "vocab_size": 421071
+}

models/embeddings/monolingual/ka_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:61d9ae428b660b7fccdf38b0f2289096a7d090120f81271237067b4a13e45d66
+size 741366152

models/embeddings/monolingual/ka_64d.meta.json

@@ -0,0 +1 @@
+{"lang": "ka", "dim": 64, "max_seq_len": 512, "is_aligned": false}

models/embeddings/monolingual/ka_64d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "ka",
+  "dimension": 64,
+  "version": "monolingual",
+  "training_params": {
+    "algorithm": "skipgram",
+    "min_count": 5,
+    "window": 5,
+    "negative": 5,
+    "epochs": 5,
+    "encoding_method": "rope",
+    "dim": 64,
+    "threads": 5
+  },
+  "vocab_size": 421071
+}

models/subword_markov/ka_markov_ctx1_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c465233e21860e1f4fb670438292b77828cbf813f41d34ae3dae859b548507eb
+size 471436

models/subword_markov/ka_markov_ctx1_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 1,
+  "variant": "subword",
+  "language": "ka",
+  "unique_contexts": 8326,
+  "total_transitions": 309141841
+}

models/subword_markov/ka_markov_ctx2_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8615344fe57b370018b7758060270beab1c7622f6e921112af1f55679db7d5a9
+size 2847457

models/subword_markov/ka_markov_ctx2_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 2,
+  "variant": "subword",
+  "language": "ka",
+  "unique_contexts": 63087,
+  "total_transitions": 308954218
+}

models/subword_markov/ka_markov_ctx3_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6c403c47b58e18dc7344289c95f9494b9ab00a4f44b9423023e7477373223811
+size 13245902

models/subword_markov/ka_markov_ctx3_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 3,
+  "variant": "subword",
+  "language": "ka",
+  "unique_contexts": 348410,
+  "total_transitions": 308766595
+}

models/subword_markov/ka_markov_ctx4_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9a64c6fd65cbb042425fa04e9f13e9fde64ef8b19d890941d6cff4b09d9476ff
+size 47629506

models/subword_markov/ka_markov_ctx4_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 4,
+  "variant": "subword",
+  "language": "ka",
+  "unique_contexts": 1604353,
+  "total_transitions": 308578972
+}

models/subword_ngram/ka_2gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c96de5b7f6b328044bd33204208c4404d4bac99d4c6bdea8465edcbb33394879
+size 264569

models/subword_ngram/ka_2gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 2,
+  "variant": "subword",
+  "language": "ka",
+  "unique_ngrams": 18881,
+  "total_ngrams": 309141841
+}

models/subword_ngram/ka_3gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eccbf3e7d4ccb1e0c4819da07b76516dd7aaf7e002bcb18c3d98c8949c1ae249
+size 2059647

models/subword_ngram/ka_3gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 3,
+  "variant": "subword",
+  "language": "ka",
+  "unique_ngrams": 159884,
+  "total_ngrams": 308954218
+}

models/subword_ngram/ka_4gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:26f668e212f640cf6f9fab5373b789c7faa3203ae2ffd61a43b1dc83c6d45029
+size 12295172

models/subword_ngram/ka_4gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 4,
+  "variant": "subword",
+  "language": "ka",
+  "unique_ngrams": 936455,
+  "total_ngrams": 308766595
+}

models/subword_ngram/ka_5gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d96bfec711a7e97434d26504298d09559b0f28a24abb82ce6edbccbdef6c378
+size 41501593

models/subword_ngram/ka_5gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 5,
+  "variant": "subword",
+  "language": "ka",
+  "unique_ngrams": 2995844,
+  "total_ngrams": 308578972
+}

models/tokenizer/ka_tokenizer_16k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:372f358b33ef68d7d170cb2704b8770921873fdaa887a7c72850be470cb1a193
+size 654357

models/tokenizer/ka_tokenizer_32k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:168f713461a034c4252da82088741421d9625f385016d545dea47103e6d328ef
+size 1102811

models/tokenizer/ka_tokenizer_64k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eacd05d8f9a31ba7e8d88719889f928292533751634e741561176084606f34d6
+size 2023396

models/tokenizer/ka_tokenizer_8k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a0e63e7666863152dd2615f8c443e14b0dcb0e70d5c637d099ea3650604caf57
+size 440122

models/vocabulary/ka_vocabulary.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e4b2882bfd95a099b8f14bc69f467f1a79a211c60f296356c8b540fa13c69b83
+size 12380098

models/vocabulary/ka_vocabulary_metadata.json

@@ -0,0 +1,17 @@
+{
+  "language": "ka",
+  "vocabulary_size": 715127,
+  "variant": "full",
+  "statistics": {
+    "type_token_ratio": 0.04298959548982624,
+    "coverage": {
+      "top_100": 0.18790042052392505,
+      "top_1000": 0.4113044643429324,
+      "top_5000": 0.6045240356095335,
+      "top_10000": 0.6858694352462464
+    },
+    "hapax_count": 930417,
+    "hapax_ratio": 0.5654160569392249,
+    "total_documents": 187623
+  }
+}